System and method for correlating collected observation campaign data with sales data

ABSTRACT

Systems, apparatuses, processes, methods and operations for implementing and managing a data collection for data regarding the observation of product or service related information. In some embodiments, the invention is directed to systems, apparatuses, processes, methods, and operations for enabling an observation campaign to be evaluated, and if desired, modified, based on sales or other data obtained from a merchant or place of business. In some embodiments, a communication or instruction may be generated and provided to a user/observer participant in an observation campaign requesting that they alter some aspect of the setting or environment in which a product is being sold or offered for sale. In some embodiments, such a communication may be provided to a proprietor of a sales location or to a campaign coordinator so that they are aware of a problem with the way a product or service is being marketed or displayed and can take remedial actions if desired.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application No.62/426,057, entitled “System and Method for Correlating CollectedObservation Campaign Data with Sales Data,” filed Nov. 23, 2016, whichis incorporated herein by reference in its entirety for all purposes.

BACKGROUND

Individuals and businesses often desire to collect data about varioussituations that exist in the real-world. For example, a manufacturer ofa product may wish to collect data about how the product is beingdisplayed or marketed at a retail location. However, individuals andenterprises, and government and non-governmental agencies, often do nothave the time and/or resources available to travel to myriad locationsor employ agents to do so to collect information. This challenge becomesgreater as the time window for the information collection becomes morespecific and/or the physical locations from which the information is tobe collected grow in number or distance. For example, merchants orentities in the delivery chain of products for retail sale, whethermanufacturers, distributors, wholesalers, brokers, or the like, may needto determine if, when, how much, in what way or condition, whetheraccompanied by marketing material, or at what price their goods arebeing made available to consumers. Another example of such a need forinformation gathered from a location might be a property or businessowner that contracts for a periodic service, about which they would liketo validate the quality and completeness of the service delivery. Inshort, the condition of physical locations or some physically observablecondition at the locations is often desirable data to observe, obtain,and document.

Sometimes these needs for data occur in a predictable or recurringpattern. In some cases, the data may also be best obtained within anarrow time window. For example, merchants who sell goods and productsthrough retailers often have guidelines, rules, and restrictionsregarding how retailers are expected to present and price the goods andproducts at the purchase location. Merchants (or other entities) maywish to negotiate with retailers for specific shelf space for themerchant's goods and products, or the merchant may wish to disallowlowering of the retail price for the merchant's goods and productsoverall, at a specific location, or during a specific time period.

Individuals and business wishing to obtain observable real-world datafor purposes of confirming the proper display, pricing, or othercharacteristic of how a product or service is being offered to consumersmay hire staff to ensure that the various standards, conditions,guidelines, rules, and/or restrictions are met. That is, employees oragents may travel to various locations at various times to observe andcollect real-world data with regard to various conditions, guidelines,rules, and/or restrictions in order to ensure satisfaction andcompliance. Of course, such employees and agents can be expensive interms of payroll and travel expenses. Therefore, organizations oftenmust rely on others (such as retailers) to follow the negotiatedconditions, guidelines, rules, and restrictions. However, at times,those other actors have little incentive to go to any great effort toensure that standards or conditions are met or to ensure compliance withapplicable guidelines, rules, and/or restrictions. As a result, manybusinesses are unable to ensure that their standards, conditions,guidelines, rules, and/or restrictions are followed with regularity orthat deviations from the approved policies or rules are noted andcorrected.

Embodiments of the systems, apparatuses, processes, methods, andoperations described herein are directed to overcoming these and otherlimitations of conventional approaches, both individually and incombination.

SUMMARY

The terms “invention,” “the invention,” “this invention” and “thepresent invention” as used herein are intended to refer broadly to allsubject matter described in this document and to the claims. Statementscontaining these terms should be understood not to limit the subjectmatter described herein or to limit the meaning or scope of the claims.Embodiments of the invention covered by this patent are defined by theclaims and not by this summary. This summary is a high-level overview ofvarious aspects of the invention and introduces some of the conceptsthat are further described in the Detailed Description section below.This summary is not intended to identify key, required or essentialfeatures of the claimed subject matter, nor is it intended to be used inisolation to determine the scope of the claimed subject matter. Thesubject matter should be understood by reference to appropriate portionsof the entire specification of this patent, to any or all drawings, andto each claim.

One or more embodiments of the invention are directed to systems,apparatuses, processes, methods and operations for implementing andmanaging a data collection campaign for data derived from theobservation of product or service related information. In someembodiments, the invention is directed to systems, apparatuses,processes, methods, and operations for enabling an observation campaignto be defined and executed. As part of that design and execution, auser's or prospective user's rating may be accessed and used to decidewhether to make a specific opportunity or set of opportunities availableto a specific user, to process the data received from a certain user ina certain way, what the compensation for participating in an observationcampaign will be, or what the rules are that determine when a campaignor an observer's participation in a campaign are completed. Further, aspart of that design and execution, in some embodiments, the invention isdirected to systems, apparatuses, processes, methods, and operations forenabling an observation campaign to be evaluated, and if desired,modified, based on sales or other data obtained from a merchant or placeof business. In some embodiments, a communication or instruction may begenerated and provided to a user/observer participant in an observationcampaign requesting that they alter some aspect of the setting orenvironment in which a product is being sold or offered for sale. Insome embodiments, such a communication may be provided to a proprietorof a sales location or to a campaign coordinator so that they are awareof a problem with the way a product or service is being marketed ordisplayed and can take remedial actions if desired.

In one embodiment, the system and methods described herein are directedto a computer-based method, where the method includes establishing anobservation campaign for the collection of real-world data regarding aproduct or service, identifying one or more users for participation inthe observation campaign, obtaining sales data related to the product orservice, correlating the sales data with collected real-world data; andmodifying an aspect of the observation campaign based on thecorrelation.

In another embodiment, the system and methods described herein aredirected to a computing system, where the system includes a user-basedmobile computing device configured to execute an observation applicationto coordinate observing and collecting of real-world data, anobservation server computer configured to push a notification thatincludes one or more opportunities for observations of real-world datato the user-based mobile computing device and configured to receivereal-world data observed and collected by the user-based mobilecomputing device, a computer-based method executing on the observationserver computer to process the real-world data observed and collected bythe user-based mobile computing device, the processing of the real-worlddata including accessing sales related data for a product or service forwhich the real-world data is being collected, correlating the accessedsales related data with the received real-world data; and based on thecorrelation or correlations, modifying an aspect of the observationcampaign, and a computing device communicatively coupled to theobservation server computer and configured to generate and control acampaign of opportunities pushed by the observation server computer.

In yet another embodiment, the system and methods described herein aredirected to a cloud-based multi-user observation computing system, wherethe cloud-based system includes a plurality of user-based mobilecomputing devices each executing an observation application configuredto communicate with a server computer hosting observation opportunities,a cloud-based observation platform that includes the server computer forhosting the observation opportunities, the observation platformconfigured to communicate one or more opportunities to one or moreuser-based mobile computing devices and configured to receive real-worlddata collected by one or more of the one or more user-based mobilecomputing devices, a computer-based method executing on the observationplatform to process the real-world data observed and collected by theone or more user-based mobile computing devices, the processing of thereal-world data including accessing sales related data for a product orservice for which the real-world data is being collected, correlatingthe accessed sales related data with the received real-world data, andbased on the correlation or correlations, modifying an aspect of anobservation campaign, and a plurality of computing devicescommunicatively coupled to the observation platform and configured togenerate and alter opportunities as part of one or more observationcampaigns, each observation campaign having configurable parametersconfigurable by at least one of the computing devices.

Other objects and advantages of the present invention will be apparentto one of ordinary skill in the art upon review of the detaileddescription of the present invention and the included figures.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the subject matter disclosed herein in accordance withthe present disclosure will be described with reference to the drawings,in which:

FIG. 1 is a basic block diagram illustrating elements or components ofan example system in which an embodiment of the subject matter disclosedherein may be implemented;

FIG. 2 is a more detailed block diagram of the basic system of FIG. 1illustrating elements or components of an example system in which anembodiment of the subject matter disclosed herein may be implemented;

FIG. 3 is a data structure diagram illustrating parameters of an exampleobservation campaign used in conjunction with the system of FIG. 2according to an embodiment of the subject matter disclosed herein;

FIG. 4 is a flow chart or flow diagram illustrating a method, process,operation or function for establishing, managing, administering, andupdating or modifying a real-world data observation campaign, based atleast in part on collecting sales data in order to generate correlationdata between the real-world data and the sales data, according to anembodiment of the subject matter disclosed herein;

FIG. 5 is a an illustration of an organization of sales data againstreal-world data from various observation campaigns, showing correlationdata that may be generated and analyzed according to an embodiment ofthe subject matter disclosed herein; and

FIG. 6 is a diagram illustrating elements or components that may bepresent in a computer device or system configured to implement a method,process, function, or operation in accordance with an embodiment of thesubject matter disclosed herein.

Note that the same numbers are used throughout the disclosure andfigures to reference like components and features.

DETAILED DESCRIPTION

The subject matter of embodiments disclosed herein is described herewith specificity to meet statutory requirements, but this description isnot necessarily intended to limit the scope of the claims. The claimedsubject matter may be embodied in other ways, may include differentelements or steps, and may be used in conjunction with other existing orfuture technologies. This description should not be interpreted asimplying any particular order or arrangement among or between varioussteps or elements except when the order of individual steps orarrangement of elements is explicitly described.

Embodiments will be described more fully hereinafter with reference tothe accompanying drawings, which form a part hereof, and which show, byway of illustration, exemplary embodiments by which the systems andmethods described herein may be practiced. These systems and methodsmay, however, be embodied in many different forms and should not beconstrued as limited to the embodiments set forth herein; rather, theseembodiments are provided so that this disclosure will satisfy thestatutory requirements and convey the scope of the subject matter tothose skilled in the art.

By way of overview, embodiments of the systems and methods discussedherein may be directed to an observation platform for coordinators,merchants, retailers, and users to establish, utilize and fulfill anobservation campaign suited to obtain, collect, and verify observable ordetectable real-world data related to standards, conditions, rules,guidelines, and/or restrictions regarding the placement, display, andadvertising or marketing of products and services. In some cases, theobservable data may be part of an overall strategy in an observationcampaign established by an observation campaign coordinator. Such astrategy may involve determining data such as (or related to) specificproduct placement, product pricing, collateral materials, and productincentives at a retail location. A participant or prospectiveparticipant in an observation campaign (referred to as a user orobserver herein) may use an application downloaded to and executing on acomputing device, to identify an opportunity to participate in anobservation campaign and/or to capture specific observation campaignrelated data and information. For example, an observation campaign mayinclude a set of verifiable parameters that define specificcharacteristics of an observation campaign strategy. These verifiableparameters may include specific observable and verifiable real-worlddata such as where a product is placed on a retail shelf, whetherpromotional materials are displayed with the product, inventory count,and/or product pricing.

Once an observation campaign opportunity is created and made availableto observers, it may be “discovered” or identified (via a suitablesearch function and/or application) and accepted by a user/observer. Theuser may then collect the real-world data through various methodsincluding, key entry, temperature observance, sound recording, videorecording, barometric pressure observance, sound-pressure levelobservance and image capture (using one or more applications executingon a computing device, such as a mobile phone). The captured real-worlddata may then be uploaded to the observation platform in order todetermine if the parameters of the observation campaign have been met(e.g., correct data, a sufficiently clear image, timely collection, andthe like). Based on an evaluation of the collected real-world dataand/or other aspects of the user's performance, each user may beassociated with a user rating that reflects the user's “quality” or“reliability” as an observer or source of data for a campaign. Further,the user's rating may increase or decrease based on a number of factors,including successful data collections, quality of collected data,utility of collected data, frequency of data collections, and timelinessof data collections. These and other aspects are described in greaterdetail below with respect to FIGS. 1-6.

FIG. 1 is a basic block diagram illustrating elements or components ofan example system 100 in which an embodiment of the subject matterdisclosed herein may be implemented. In the context of FIG. 1 and otherfigures, a continuing example of a merchant-based observation campaignwill be used. Thus, various real-world data points specific to amerchant that sells goods at retail locations will also be used in thiscontext. However, a skilled artisan will understand that the coordinatorof an observation campaign need not be a merchant with merchant-specificdata points. For example, a coordinator of an observation campaign maybe a quality-assurance coordinator seeking to collect data aboutinstallations of observable projects, e.g., power transmission poleinstallations, bathroom conditions in a public transit location, trafficconditions, and the like. In short, a coordinator of an observationcampaign may be any individual, business, government agency, or entitythat wishes to incentivize observers to collect real-world data frommyriad locations. However, for ease of illustration and for consistentexamples, the remaining disclosure will use the merchant product andretail location example without limiting the interpretation or scope ofthe claims or specification.

As discussed briefly above in an overview, an observation platform 120provides systems and methods for a merchant 110 to propose, establish,and implement an observation campaign for various observers 130, 131,and 132 to collect real world data about the observation campaignestablished by the merchant 110. In this system 100, the merchant block110, the observation platform block 120 and each observer block 130,131, and 132 may represent a separate computing device or group ofcomputing devices. Further, the connections between these computingdevices may be any communication link, such as the Internet, BlueTooth™wireless, direct serial link, and the like. The various computing deviceimplementations and embodiments are discussed further below.

The observation platform 120 may be embodied in whole or in part as acomputing system that includes one or more server computers configuredin a cloud-based computing environment. Embodiments may take the form ofa hardware implemented embodiment, a software implemented embodiment, oran embodiment combining software and hardware aspects. Further, variouscomputing aspects of the underlying systems and methods may transcendany specific computing entity and the “block” diagram nature of FIG. 1is intended to illustrate one embodiment. For example, in someembodiments, one or more of the operations, functions, processes, ormethods described herein may be implemented by one or more suitableprocessing elements (such as a processor, microprocessor, CPU,controller, etc.) that are part of a client device, server, networkelement, or other form of computing or data processing device/platformand that is programmed with a set of executable instructions (e.g.,software instructions), where the instructions may be stored in asuitable non-transitory data storage element. In some embodiments, oneor more of the operations, functions, processes, or methods describedherein may be implemented by a specialized form of hardware, such as aprogrammable gate array, application specific integrated circuit (ASIC),or the like. This detailed description is, therefore, not to be taken ina limiting sense.

As alluded to, in some embodiments, the subject matter may beimplemented in the context of a “cloud” based computing environmenttypically used to develop and provide web services and businessapplications for end users. Further aspects of an exemplaryimplementation environment will be described with reference to FIGS. 2-5below. Note that embodiments may also be implemented in the context ofother computing or operational environments or systems, such as for anindividual business data processing system, a private network used witha plurality of client terminals, a remote or on-site data processingsystem, another form of client-server architecture, and the like.

The merchant computing device 110 may also be one or more of any numberof computing device implementations. In one embodiment, the merchantcomputing device 110 may be a personal computer or mobile computingdevice. The merchant computing device 110 may be communicatively coupledto the observation platform 120 through an Internet connection or othercommunication network. Further, the merchant computing device 110 may beconfigured to execute a merchant observation application (not shown)that may provide various graphical user interfaces (GUIs) for a merchantto navigate and utilize the observation platform 120, such as, forexample, defining and establishing an observation campaign. Variousoperations and parameters of the merchant computing device 110 aredescribed further below with respect to FIG. 2.

Each observer computing device 130, 131, and 132 may also be one or moreof any number of computing device implementations. In one embodiment,each observer computing device 130, 131, and 132 may be a mobilecomputing device (such as for, example, a mobile phone, a smart phone, atablet, or a laptop computer). Each observer computing device 130, 131,and 132 may be communicatively coupled to the observation platform 120through an Internet connection or other communication network. Further,each observer computing device 130, 131, and 132 may be configured toexecute an observer application (not shown) that may provide variousGUIs for an observer to navigate and utilize the observation platform120, such as, for example, fulfilling an opportunity in an observationcampaign by accepting the opportunity and submitting observed andcollected data, etc. Various operations and parameters of the observercomputing devices 130, 131, and 132 are also described further belowwith respect to FIG. 2.

FIG. 2 is a more detailed block diagram of the basic system of FIG. 1illustrating elements or components of an example system 200 in which anembodiment of the subject matter disclosed herein may be implemented.Some elements of FIG. 1 are also shown in FIG. 2 and bear the samereference numerals. In particular, the observation platform 120 may becoupled to computer network 205 such as the Internet, just as observercomputing device 130, 132, and 132 are also connected via the network205. A skilled artisan will understand that the designation of observer1 130 through observer n 132 indicates that any number ofobservers/users may be shown as interfacing with the system 200 (whethersuch interfacing is discrete or collaborative). In this system 200embodiment, more than one merchant computing device is shown. Thus,merchant computing devices 210, 211, and 212 may represent differentmerchants who may individually and independently craft observationcampaigns using the observation platform 120 via the network 205. Askilled artisan will understand that the designation of merchant 1 210through merchant n 212 indicates that any number of merchants may beshown as interfacing with the system 200.

Additional computing devices may also be communicatively coupled to theobservation platform 120 via the computer network 205. As will bediscussed further below, the system 200 may include a number of retailercomputing devices 240, 241, and 242. A skilled artisan will understandthat the designation of retailer 1 240 through retailer n 242 indicatesthat any number of retailers may be shown as interfacing with the system200. Further, the system 200 may include computing devices associatedwith third party services 250, private services 251, and governmentservices 252. Prior to discussing the additional roles for theseadditional computing devices, the observation platform 120 as well asthe observers' and merchants' roles are discussed in greater detail.

By way of a general overview of the system in FIG. 2, one embodiment mayinclude a user-based mobile computing device 130 (e.g., a mobile phone,a smart phone, a laptop computer, tablet, or handheld computer, awearable computing device, an augmented reality device, and the like)configured to execute an observation application to coordinate observingand collecting of real-world data. Further, the system may include anobservation server computer 120 configured to send notifications thatinclude one or more opportunities for observations of real-word data tothe user-based mobile computing device(s) 130 and configured to receivereal world data observed and collected by the user-based mobilecomputing device(s) 130. Further yet, the system may include amerchant-based computing device 210 communicatively coupled to theobservation server computer 120 and configured to generate and control acampaign of opportunities sent by the observation server computer 120.

As briefly discussed above, the observation platform 120 may be one ormore computing devices configured to work as a server computing entityin a cloud-based computing environment to establish and facilitateobservation campaigns for one or more merchants. As used throughout thisdisclosure, an observation campaign is a set of parameters establishedby a merchant using one or more inter-related applications operating inthe system 200 and coordinated from the observation platform 120. Theobservation campaign may include a number of parameters designed toencourage various remote users of an observation application (e.g.,observers interacting with a mobile device in which is installed such anapplication) to collect real-world data about products and goods forsale at various retail locations, or the status of a service beingprovided at a location.

The campaign may collect both qualitative and quantitative informationfor calculating ROI on marketing expenses. Qualitative examples mayinclude: is the display appealing, did you like the taste of the yogurt,does the security make you feel safe and if not why not? Quantitativeexamples may include: Is the price 5.99, what is the price, what is thecount, service took less than 5 minutes, the lawn was mowed, was the ATMwas working. The data format of the campaign is standardized so that theanswers can be compared both across locations, but also across time(historical) in the same locations. The campaign may be to compare theresults against a standard or assist in validating a prediction.

In some embodiments or use cases, a merchant that establishes anobservation campaign using the observation platform 120 desires to knowmore about the retail locations and point of sale for their goods andproducts in the marketplace. For example, a merchant may negotiate adeal to have products placed in specific eye-level locations on shelvesat retail locations and may wish to verify that the products are, infact, placed on eye-level shelves at the various retail locations. Inother cases, a goal of a campaign might be to measure project progress(is the construction site leveled?), determine asset status (is a backuppower generator present in all building locations?), evaluate supplychain delivery (was the correct medicine delivered without being exposedto heat?), validate a machine learning prediction (did the computeraccurately predict the size of the queue at the cash register atlunchtime?).

By establishing an observation campaign that leads to the collection ofreal-world data (e.g., digital images of the product for sale oneye-level shelves at a particular retail location), the merchant canincentivize observers (e.g., through compensation offered by theobservation platform) to fulfill the requirements of the specificreal-world data collection. That is, the merchant may implement anobservation campaign through a contract with a proprietor or operator ofthe observation platform such that the observation platform then offersmonetary compensation to one or more observers in exchange for a timelyand meaningful collection of real-world data (e.g., a picture) of themerchant's product for sale at a specific retail location. Thiseliminates the need for the merchant to employ one or more individualsto travel to the retail location to collect and verify such real-worlddata. In a sense, this is a form of crowd-sourcing the data collectionprocess for a campaign.

Note that as used herein, the term “real-world data” may refer tospecific observable facts or data about anything of interest to thecoordinator of the campaign. Real-world data may be data that can becollected in the real world (as opposed to data that may reside on aretailer's computer). In one example, real-world data may be a digitalimage of a product for sale at a retail location such that useful datamay be gleaned from the image, such as the product location on a shelf,the product location in proximity to other products, if the productlabel is facing outwards, if the product price can be seen, if theproduct price is correct, if additional promotional material isproximate, and the like. Real-world data includes, but is not limitedto, any observable verifiable data collectable through any manner ofsensing, recording, or observing.

In this manner, a merchant can use the observation platform to design anobservation campaign to incentivize observers to collect very specifickinds of real-world data at very specific locations over very specifictime frames. In one example, when designing an observation campaign, themerchant may establish observation opportunities based on a desired timeframe; for example, a campaign may only have opportunities offered for aone-week time frame or just a few hours in any given day. Further, themerchant may establish observation opportunities based on a desiredlocation such as retail locations in a specific city or within 100 milesof a particular location. Further yet, the merchant may establishobservation opportunities based on a desired total number ofobservations—e.g., a “cap” of 10 different successful observations.Other possible campaign parameters are possible and discussed furtherbelow with respect to FIG. 3.

As opportunities for observation become active (that is, users of theobserver application may be offered opportunities via the application)various observers may engage in the various opportunities. Opportunitiesmay be sent as notifications to observers (via a smart phone applicationand the like). Such notifications may be influenced or determined bygeographic location of various observers (e.g., notifications are onlysent to observers proximate to a desired observation retail location).Further, such notification may be influenced by a relative reputation ofan observer (e.g., only the “best” or most effective observers receivecertain opportunities, such as those having a user rating above aspecified threshold value).

A basis for deciding whether or not to notify or invite someone toparticipate in an observation campaign might also include: when was thelast time they did an observation, how many have they had rejected, whatdid they get paid to do a similar observation, what type of equipment dothey have (high vs low quality camera), what are their provable skills(speak a certain language).

Once an opportunity is received or otherwise made known to a prospectiveuser/observer (such as by receiving a notification generated by theObservation Campaign Platform, performing a search of a set of availableopportunities, receiving a message via a messaging application, etc.),the observer may accept the opportunity and begin an observation (i.e.,a collection of data or information). Some opportunities may have timelimits for acceptance of the opportunity and/or time limits forfulfillment of an accepted opportunity. In some examples, theobservation may be executed by simply sending the requested real-worlddata to the observation platform 120 for evaluation. The observationplatform may include a module for determining 260 whether the receivedreal-world data from any observer meets the requirements and parametersof an opportunity in an active observation campaign. The determinationmodule 260 may be configured to determine if the received real-worlddata from the observer (e.g., sent from a user-based mobile computingdevice) fulfills one or more criteria for data collection in theobservation campaign opportunity.

Such criteria may include, but are not limited to (or required toinclude) timeliness, image quality, data applicability, datacompleteness and the like. Note that the criteria may depend uponcharacteristics of the campaign, but in general relate to a provablequality of the data. For example, if the data is an image, is it notblurry and of the right areas in a store?; if the data is a video, is itlong enough?; if the data is an opinion, is it clearly written; if thedata is quantitative, does it match what shows up in thepictures/videos; were the campaign instructions followed correctly?;timing issues—how long did it take the person to obtain the real-worlddata after they accepted?; were there unusual support needs—did theperson require excessive assistance to complete the tasks involved? Notethat in some cases, any problems with the collection process or dataquality may lower a user rating; for example, accepting an observationopportunity and not properly completing the tasks will typically lower auser's rating. Conversely, accepting opportunities and completing thetasks quickly and correctly may raise a user's rating, as wouldreceiving positive feedback from an end customer.

If the determination module 260 determines that the received real-worlddata from an observer meets the requirements of an observationopportunity, (e.g., the opportunity has been fulfilled), then acompensation module 261 may initiate compensation to the observer forsuccessfully fulfilling an observation campaign opportunity. Suchcompensation may be financial or non-financial. In one embodiment, thecompensation module 261 may communicate with one or more third-partyservices to credit a bank account associated with the observer orobservers that fulfilled the opportunity. Further, the compensationmodule may also track total financial compensation paid to specificobservers and communicate said total to government services 252 on anannual or other periodic basis for purposes of compliance with anyrelevant regulations or laws. In other embodiments, the compensationmodule 261 may communicate with other private services 251, such associal media or online retail, to provide non-financial remuneration tothe observer. For example, the observer may qualify for discounts orprivileges at third party locations (e.g., food coupons, gift cards,free entry into a sporting event, and the like).

FIG. 3 is a data structure diagram illustrating parameters of an exampleobservation campaign 300 used in conjunction with the system of FIG. 2according to an embodiment of the subject matter disclosed herein. Asdiscussed above, one or more merchants (210-212 of FIG. 2) may establishand coordinate an observation campaign with several differentconfigurable parameters. FIG. 3 is an illustration of a data structureof one embodiment of an observation campaign 300. It is understood thatthe parameters shown in FIG. 3 are illustrative and any number or typeof parameters (either similar or different, and greater or fewer thanshown in FIG. 3) may be present. These parameters assist in shaping howobservers will be incentivized to collect real world data in response toopportunities presented in the campaign 300. It is also understood thatcertain identification and demographic parameters (such as merchantname, merchant product, bank data, and the like) may also be establishedwith respect to the merchant. These are not discussed in further detailhere as the focus of FIG. 3 is on the customizable parameters of anobservation campaign 300.

A first parameter by which a merchant (or other entity seeking toincentivize the collection of real-world data for purposes of confirmingproper adherence to rules or requests regarding the display or othercharacteristics of a product being offered for sale at a location) maycustomize an observation campaign 300 is a total cost outlay 310. Inthis respect, the merchant and the proprietor of the observationplatform reach an agreement about the total cost of the observationcampaign. The observation platform may then set specific compensationrates for successful observation in order to meet the needs of theestablished observation campaign. Further, the merchant or theobservation platform may choose to designate a maximum (or minimum)amount of financial compensation to be awarded to an observer orobservers for successfully retrieving real-world data in fulfillment ofan observation opportunity. This may be an aggregated amount on a percampaign basis. For example, a merchant may wish to collect data aboutas many product placements as possible until a threshold amount of moneyhas been reached (in terms of financial compensation to all observers inan aggregate manner). This total may also be an aggregate cost outlayfor non-financial outlay (e.g., a limit of 10 discount coupons awarded).

The observation platform operator or manager may also change thecompensation offered based on user rating or timing; for example, thecompensation offered may be adjusted based on how much time is left inthe campaign or the value of a user rating. In one case, thecompensation might start at a price of $5.00, which would be offered for5 days; if this failed to attract enough participants or qualifiedparticipants, then the compensation offered might be increased by $1 totry and complete the campaign. Similarly, the compensation offered mightdepend upon a user or observer rating; in this case, it may be desiredto attract a certain number or percentage of “qualified” or morereliable observers by offering greater compensation to those having ahigher rating or a rating above a specified threshold.

In addition to the total cost outlay, the merchant may also configure aparameter associated with a total cost per observation 311. In thismanner, the individual observation may be capped at a maximum amount offinancial or non-financial compensation. The merchant may also define aminimum cost per observation and can establish a sliding scale for costper observation according to a number of different variables. Forexample, the merchant may choose to pay more per observation for thefirst 10 observations and then drop the cost per observation. As anotherexample, the cost per observation may be higher during a particular timeperiod, such as late night and then lower during a different timeperiod, such as morning. Thus, additional parameters regarding variablecost per transaction 317 may be configured.

The merchant may configure a parameter associated with a total number ofobservations 312. In this manner, each observation may be counted andonce a limit has been reached, the campaign may be suspended orterminated. The merchant may also define a minimum number ofobservations needed in order for a campaign to end at a time limit orwithin a time frame 313. Still further, the merchant may establish afixed time frame 313 in which the campaign must begin and end. Forexample, the merchant may choose to establish a time frame of June1^(st) to June 30^(th) for which a total number of observations islimited to 1000; in this case, once 1000 observations are reached, thecampaign may end. Also, the merchant may establish 100 observations as aminimum threshold in which case, the campaign can only end on June30^(th) if 100 observations are entered.

The merchant may configure a parameter associated with a geographiclimitation or boundary 314 of the campaign. Several geographicparameters may be established, such as a relative geographic limit of astate, city of country or an absolute geographic limit of within 500miles of a specific location (company headquarters, for example). Theremay be additional parameters about limiting the number of observationwithin geographic regions or limiting the total cost outlay associatedwith a geographic region. Further yet, cost per observation may beconfigured on a per region basis (e.g., paying more for region 1 thanregion 2).

The merchant may configure a parameter associated with a diminishingcost per observation 315, choosing to pay more for initial observationsas compared to later observations. In another example, the parametersmay be reversed to pay more for each additional observation after acertain threshold has been reached. Further, the merchant may limitobservers who can participate in a campaign to only those observers whohave reached a specific observer reputation score or ranking 316.

Additional parameters in a campaign may include failure parameters 318that may act to suspend or terminate a campaign if a threshold of failedobservations is entered or the real-world data collected in initialobservations indicates that additional failures will be imminent orpredictable. There may be restrictions on the type and quality of imagesreceived from observers based on image quality parameters 319. Themerchant may establish opportunity lockout parameters 320 for observerswho accept an opportunity and are then given two hours of exclusivity tothe opportunity to fulfill it. Lastly, in this embodiment, a merchantmay define a successful campaign termination parameter 321 so that acampaign may end when a threshold number of successful observations hasbeen reached or a statistically valid number of successful observationsis reached. Note that there are additional parameters that may beconfigured in an observation campaign 300, but are not described in asmuch detail herein.

As an example, suppose that a campaign is measuring several store brands(e.g., Safeway, PCC) and it is noticed that based on data collected foran observation campaign, one of the two is performing poorly/perfectly.In this case, the campaign may be temporarily suspended to provide timefor an investigation into the causes or issues involved. Similarly,campaigns may be increased in scope/size based on failure rates, andrecurring campaigns may be started/paused based on failure rates.Further, based on ongoing results of a campaign, the questions, taskdescription, locations for data collection may be changed, added, orreordered automatically by the platform/system. Note that a machinelearning model or another form of data analysis may be used to improvethe selection of the primary or secondary campaign parameters. Forexample, based on historical data and the campaign parameters (type ofproduct, locations [rural, urban, etc.]), a campaign coordinator maychoose locations based on predicted bad locations. Questions can bechanged, added, or reordered based on predicted results. Pricing of thecompensation may be set based on the predicted speed/quality of theobservers that would be expected to perform the observations and datacollection.

FIG. 4 is a flow chart or flow diagram illustrating a method, process,operation or function for establishing, managing, administering, andupdating or modifying a real-world data observation campaign, based atleast in part on collecting sales data in order to generate correlationdata between the real-world data and the sales data, according to anembodiment of the subject matter disclosed herein. In one exampleimplementation, a coordinator of an observation campaign may establishspecific sales data collection targets along with real-world datacollection targets. These sales data collection targets may specifyspecific sales data that is desired to be monitored or collected, alongwith the products or services whose sales are being collected, atimeframe in which collection is desired, and any other parameters thatmay be relevant to a campaign and/or to a place of business oroperations where a product is being offered or displayed for sale. Inthis manner, the sales data collected contemporaneously with thereal-world data (or reflective of the real-world data) may be used togenerate correlation data for an observation campaign. That is, acampaign coordinator or other party may analyze and measure aneffectiveness of an observation campaign over time by generatingcorrelation data for the observation campaign in terms of resultantsales data or sales data changes at various points in time.

However as recognized by the inventors, in a complex environmentimplemented on a cloud platform involving many unrelated individuals, agreat amount of guess work may be involved in establishing the initialvalues of the parameters of an observation campaign. This introduces anelement of uncertainty with regards to the effectiveness of the campaignas it is initially formulated and with regards to the value of theinformation collected, since information may satisfy the parameters ofthe observation campaign but fail to be satisfactory for use in furtherprocessing or evaluation. Thus, a campaign coordinator may alsoestablish metrics involving specific parameters of the campaign (assuggested by step or element 472 of FIG. 4), where a result of comparingthe collected real-world data (or information/metadata about thatreal-world data) to one or more of the metrics may result in altering ormodifying a parameter of the campaign.

As an example, an observation campaign may establish a specificgeographic location having a perimeter surrounding a map point (e.g., aradius of one mile surrounding a location). In one embodiment, anypotential observer that travels into the one-mile radius region may thenreceive an alert via an observation application executing on theobserver's smart phone, with the alert indicating that an observationopportunity is available. However, a metric may be established thattracks the number of observations performed over a set time period.Thus, if 24 hours pass without a single observation (or without asufficient number to indicate that enough information has beencollected), this metric may be used as the basis for altering theone-mile radius parameter that controls which potential observers arenotified. For example, the radius may be increased to two miles or threemiles and then the collected information or number of fulfilledobservation opportunities again compared to the metric for theparameter.

As another example, an observation campaign may be established providingopportunities to observers that offers a $5.00 payment in return for avalid observation. After a set period of time or after a set number ofsubmitted observations, the rate of successful (i.e., acceptable orvalid) observations over the time period may be analyzed against ametric. In one embodiment, this analysis may determine that theobservation campaign is returning more observations than what isrequired for meaningful, statistical conclusions to be drawn about thereal-world data. As a result, the observation campaign parameterregarding payment offered per observation may be reduced from $5.00 to$3.00 to reduce the number of potential observers being incentivized tocollect data for that observation campaign.

Thus, a system and method may be established wherein the observationcampaign platform receives electronic communications from one or moreremote computing devices (user's/observer's smart phones, for example)wherein each electronic communication includes real-world datacorresponding to parameters of an observation campaign (such as an imageof a product on display, an observer's notes regarding the generalcondition of the location, a description of collateral informationavailable near the product, etc.). The observation campaign platformthen analyzes the received real-world data against a metriccorresponding to (such as being based on, derived or generated from, orincorporating) one or more parameters defined for the campaign, anddetermines whether any parameter should be changed to increase thelikelihood of a successful campaign.

As suggested by FIG. 4, in some embodiments, an observation campaign maybe established or defined by a manufacturer, distributor or provider ofa product or service at an observation campaign platform (as suggestedby step or stage 402). For example, the observation campaign may beestablished by an observation campaign coordinator (an entity wishing toknow about the presentation/display of retail products offered for saleat retail locations) who may select a number of campaign parametersdefining an overall observation campaign; that is, the observationcampaign may be described or defined by certain parameters orcharacteristics (such as those represented by the data structureillustrated in FIG. 3).

As shown in FIG. 4, such parameters or characteristics may include alist or description of the real world data to be collected (as suggestedby element 470, and for example, an image of a product on display andits surroundings), the parameters of that data (471) (such as image sizeor resolution, time at which image or other data is desired to becollected, etc.), relevant metrics for evaluating the validity orutility of collected data (472), and a measurement threshold value usedto decide what amount constitutes a sufficient number (minimum ormaximum) of measurements for the campaign (473) or for alteration of aparameter of the campaign.

As suggested, the various metrics (472) defined or described by acampaign coordinator may provide tools for analysis and evaluation ofthe collected real-world data, where such analysis may result in themodification of one or more campaign parameters or characteristics. Asmentioned, the campaign coordinator may establish various measurementthresholds (at step 473) such that an established threshold is to be metbefore an analysis is triggered that may result in altering a parameter.That is, a specific number of observations (e.g., a threshold ofobservations) may be needed before any meaningful analysis can be (orshould be) conducted that results in the observation campaign parametersbeing altered.

Once all real-world data settings, parameters, metrics and thresholdsare established or defined, the observation campaign may be “launched”(as suggested by step 404), which refers to the process of makingparticipation in the campaign available to users. Launching theobservation campaign may include storing all established settings,parameters, metrics and thresholds in a data store at, or otherwiseassociated with, the observation campaign platform. In some embodiments,the observation campaign parameters and settings may be searchable by anobservation campaign application executing on one or more remote mobilecomputers to enable prospective users/observers to identify observationopportunities of interest to them. In other embodiments, electroniccommunications may be generated and sent to remote mobile computers thatmeet a specific set of criteria (e.g., being within a geographiclocation, being associated with a user/observer having an observerreputation or rating score above a certain threshold, and the like) (assuggested by step 405). In some embodiments, user rating data may beaccessed and utilized to identify one or more prospective participants,or to set certain campaign parameters (405).

As observers respond to opportunities presented through the nowdiscoverable or accessible observation campaign, real-world data may bereceived and collected, where this real world data is collected oracquired by one or more users/observers (406). The data received by theobservation platform may then be subjected to one or more “tests”,evaluations, or comparisons to determine if it is valid data (408). Forexample, an assessment may be made to determine if a received digitalimage is in focus or captures the correct real-world data. As anotherexample, metadata about the real-world data, such as timestamp, and IPaddress origination and path may be used to validate the receivedreal-world data. If the data (or a portion of it) is found to be valid,then the valid data is typically assimilated or integrated into adatabase containing real world data collected for that particularcampaign (410 and 420). If the data (or a portion of it) is found to beinvalid, then the invalid data may be rejected and not subject tofurther analysis (412).

Note that the database in which the collected (and valid) data is storedis maintained by the observation platform (420), and may includedemographic data corresponding to one or more of the user/observer, thepoint of sale location, the competitive products, etc. In someembodiments, sales and additional demographic data may be collected byusers/observers, with the sales data being collected approximatelycontemporaneously with the product or sales environment relatedreal-world data (430). That is, the sales data is intended to representthe approximate sales or sales trends at the time at which thereal-world data is collected.

Next, the process determines or identifies one or more correlationsbetween the collected real-world data and the approximatelycontemporaneous sales data (440). These correlations may be identifiedby suitable data processing, such as by matching the time period orinterval over which data was collected to sales data for that period orinterval, or sales data for the period leading up to an observationcampaign. In general, the one or more embodiments measure or observeattributes (examples: on shelf, not on shelf, pricing correct, productin stock, product displayed correctly, location in relation tocompetitor, location on shelf, etc.) and then use sales data andoutcomes (examples: sales above/below expectations, sales above/belowaverage for similar stores, sales above/below prior period, etc.) toexamine relationships between the attributes and the sales data oroutcomes. To determine a mathematical or other form of relationship(such as “If an attribute has a value of X, then sales is likely to bealtered by Y”) between attributes and sales data or outcomes,embodiments may use one or more techniques such as machine learning,neural networks, genetic algorithms, linear/non-linear regression, etc.In some cases, the observation platform may contact one or more of auser/observer, an agent, a merchant, or a client of the platform (suchas a product manufacturer or distributor who requested the campaign) toprovide an analysis of the results and in some cases, provide arecommended course of remedial or corrective action (450).

Based on the analysis of one or more of the real-world data, the salesdata, or the demographic data (if collected or available), a real-timecommunication (as suggested by step 450) may be generated to communicatethe results of the analysis to any number of recipients. Thiscommunication may be in the form of a change to a display of a mobiledevice of a user that just collected the real-world data (e.g., theapplication receives an update or notification that is presented to theuser via the application or other communication protocol in the phone).The communication may suggest to the user/observer to collect otherdata, to verify certain information, to request that the proprietor ofthe location where the data was collected contact the observationcampaign coordinator, etc.

Note that examples of possible determined or identified correlations mayinclude one or more of the following, where the noted possiblecorrelation between sales data and observed data may be relevant todeciding whether to modify a campaign or contact an observer, a POS, ora client of the observation platform:

-   -   Sales are above expectations when new marketing material is        presented correctly in 20% of stores, so marketing campaign is        extended to all stores. Without the benefit of the observation        platform, it may not have been possible to notice the initial        sales increase, due to many of the test stores not displaying        material correctly;    -   A family of products is displayed as expected in the store, but        is without sufficient inventory for purchase a certain        percentage of the time—without the observation platform, the        stores and frequency of unavailable inventory for purchase would        not be known and the relationship to sales level unable to be        determined; or    -   Sales of Cereal X are on average $1000 per store per month in        California—one of the stores suddenly has lower sales, and with        the monthly observations as managed by the observation platform,        it is noticed that the Cereal is on the bottom shelf in that        store only that month. In response, the store is contacted and        the Cereal is moved to the eye-level shelf that was part of the        contract with the store chain headquarters.

As another example, observation data may be collected, with sales datarepresenting the sales of a product or set of products at a baselinetime also being collected (i.e., prior to the observation campaign beingexecuted or if already executed, prior to any further modifications).The observation campaign is then implemented (or modified andre-implemented), which may lead to changes in the arrangement,placement, or display of a product and any related information (such ascoupons, incentives, collateral materials, etc.) from the configurationprior to the campaign (or prior to the modified campaign). The salesdata is then collected again after the modifications, and as a result,the effectiveness and value of the campaign may be demonstrated inincreasing sales, reducing losses, stimulating the purchase of multipleitems, encouraging the use of a coupon or other promotional item, etc.

Note that the observation and sales data and any analysis demonstratinga relationship or correlation between the two may be shared with thestore buyer to help bring non-compliant or dissimilar locations to adesired standard of operation, to negotiate for additional productspace, to negotiate for modification to product placement, to negotiatemodification to restocking frequency, to negotiate changes to store backstocking levels, to negotiate frequency of delivery of restock to storelocations, etc.

As noted, in one embodiment, an observation campaign may be establishedand operated in a manner consistent with the steps of the process flowor method illustrated in FIG. 4. Once enough real-world data iscollected as part of a given observation campaign, sales data may becollected and processed, where the sales data represents sales or salestrends that are substantially contemporaneous with the collectedreal-world data. That is, sales data may be tracked or compared againstthe collected real-world data over time (e.g., October sales vs Octoberobservations). After enough data is collected and compared, correlationdata representing a relationship between the sales data and the observedreal-world data may be identified and assembled. This data may beanalyzed to assist in making decisions related to product placement,product advertising, etc. For example, one may identify an emergentpattern showing an increase in sales when observations are above aspecific threshold (which might indicate a need to correct certainaspects of the product display or marketing on a regular basis to ensurehigher sales). As another example, one may identify a drop in sales whenthe price per observation falls below a specific threshold (which mightindicate that prospective observers are not interested in makingobservations in sufficient numbers or at the most desirable times toensure that the observations are able to identify and correctpotentially harmful point of sale factors on an efficient basis). Thecollected and correlated data may be communicated to retailer andobservation campaign coordinators, along with suggestions for improvingsales and/or modifying the observation campaign parameters.

Other examples of uses of sales data as part of influencing anobservation program, or as indicators of a desired change to anobservation program parameter or implementation include, but are notlimited to:

-   -   If a promotional campaign is failing to produce expected        results, normally the campaign would be cancelled after sales        data is received.

By using the benefits of the observation platform, the company couldknow that the campaign was not actually happening, so instead ofcancelling the campaign and losing the investment, they can communicatewith the stores involved and work to get the campaign properly in place.

Other examples of how an observation campaign might be altered based onthe collection of sales related data, or the sales data chosen to becollected altered based on the campaign include, but are not limited to:

-   -   A campaign may be cancelled or moved to an area where there is a        larger possibility of success;    -   Sales data may be chosen based on filtered or aggregated        characteristics of the observation data. By recognizing patterns        in the observation data, one can start to look for reasons to        further analyze sales data for causation;    -   Note that machine learning may be used to relate sales data to        characteristics of a sales location (i.e., sales as a factor of        various POS characteristics used to suggest modifications to        certain of those characteristics);    -   Some attributes of a sales location are publicly available        (population, etc.), while others can be measured real-time and        on-site by an observer (e.g., is there product, is the box on        its side, did the poster get put up close enough to the product,        etc.). Machine learning can be used to look at location        attributes without overweighting some, as is sometimes the case        with a use of simple linear regression;    -   Note that sales data may be processed or evaluated to identify a        correlation with the observation campaign parameters by any        suitable method or process, including, but not limited to:    -   The observation platform can receive or access the data in a        suitable form (CSV, DB, API calls, etc.) and then the platform        uses one or more of the described mathematical operations or        techniques to find correlations;    -   The data processing may also (or instead) be performed on the        customer side using techniques they have available after        downloading the data from the platform.

FIG. 5 is an illustration of an organization of sales data againstreal-world data from various observation campaigns, showing correlationdata that may be generated and analyzed according to an embodiment ofthe subject matter disclosed herein. In this diagram, three differentobservation campaigns (elements 502, 512, and 522 in the figure) may beanalyzed against three different sets of sales data (elements 504, 506,and 508 corresponding to Campaign A, and similarly for Campaign B andCampaign C). This results in three different sets of correlation data,one for each set of sales data (elements 505, 507, and 509). Thecorrelation data may be established as correlating a set of parametersfrom the observation campaign real-world data against the collectedsales data corresponding to substantially the same time frame(s).

Examples of data that may be collected and processed to evaluate itscorrelation with sales data includes, but is not limited to (or requiredto include) the following. How the processed or evaluated data mightimpact the parameters of a campaign is also noted. Note that this couldpermit the implementation of a feedback loop between campaignparameters, the data collected, and actual sales data.

-   -   Total number of observations against total sales.    -   May indicate foot traffic in an area, so potential buyers.    -   Total number of observations against sales by region.    -   May indicate how one store in a region is more popular than        another, even though general demographic data would say they are        the same.    -   Price offered per observation against total sales.    -   Lower prices generally indicate a more popular region as there        are more observers willing to take the task on.    -   Note that this may also represent a more economically depressed        area.    -   Demographic data of region and price per observation against        regional sales data.    -   Demographic data is generally older (US census), and one can get        more accurate measurements of demographics by polling observers        in the region.    -   This may also represent a more economically depressed area.    -   Observation failure rate against total or regional sales data.    -   Higher failure rate is generally correlated with lower sales,        but not always.    -   Sales data within a region with failure rate can point out        specific stores with compliance issues.    -   If product is regularly out-of-stock, may indicate a very        positive sales opportunity for that region, as the product is        popular. High failure rate with POS data showing selling out of        product means store should order more product.    -   Retailer compliance record against same store sales.    -   Retailer compliance with product placement standards/agreement        can change from period to period and the ability to capture        observations over time will demonstrate correlation of sales        impact to compliance.    -   Retailer compliance record against user rating of observers.    -   Observers with higher rating generally delivery better        information enabling stronger correlation between compliance and        sales.    -   Sales data against user rating of observers.    -   Based on the ability of observers with higher ratings to capture        more complete information that delivers a stronger correlation        between compliance and sales—having the ability to attract        higher user rated observers to perform observations will impact        the ability to capture better information allowing for stronger        correlation(s), and therefore the ability to affect changes to        store execution so that they comply with agreed upon standards,        resulting in higher sales.    -   Cross-product sales data against observations on a single        product.    -   Knowing that the promotional campaign of another product is        often paired with the customer's, the sales data can be        correlated for information. For example, the successful        execution of a campaign for tonic water can be correlated to        higher sales of Gin X in locations with a measured success in a        campaign and lower for those that failed.    -   Retailer feedback rate against total sales data.    -   Some retailers only report sales data on a quarterly basis. If        the only way BrandX measures the success of the campaign is on        sales data, they will not be able to spot deviations in the        campaign and correct them within a selling period or sales        window.    -   Retailer sales data against differing observation campaigns.    -   If the platform is being used to measure or observe different        products in the same retailer, correlations can be drawn based        upon the actual and predicted compliance rates of that retailer        on all products/campaigns in their stores, individually and        across the chains.    -   Demographics of failure rates against demographics of success        rates.    -   This allows the customer to separate out the perceived effects        of demographics when in reality, it may be the compliance rate.        For example, if sales went really low in “poor” areas, a        customer might think campaign didn't work because of “poor”        demographics. Instead, using the observation platform, they may        be able to determine that the reality is that the “poor” demos        just didn't put the campaign material out, so the initial        correlation was proved wrong.    -   Sales data against notification to retailer rate.    -   Given that the platform can send notifications based on any        frequency chosen by the customer, this can influence both sales        and the data being looked at. Also, the notifications may be        positive or negative (e.g., negative for corrective action and        positive for positive reinforcement).    -   Demographic data against top failure regions.    -   Perceived correlation such as the relationship of demographic        data to the sales of a particular item, product type, or        category can be questioned and even refuted based on the capture        of observations, allowing the measurement of other variables        that show a stronger correlation to sales. Therefore failure        rates for stores, or regions, may not necessarily be related to        demographic data and may instead be due to other factors        captured in the observations.

Embodiments of the system and methods disclosed herein focus on drawingspecific conclusions about the data collected from the real world. Forexample, collected and verified data may be compared to sales data;point of sale (POS) locations that fail to properly use promotionalmaterials may show reduced sales. Further, the data collected may beused to identify failure rate demographics as well as demographicfeedback on specific failure points. This may lead to providing retailerfocused feedback after a failure is identified. Further yet, one coulduse multiple campaigns for cross-product promotions. As data iscorrelated with retailers, the retailers may develop a rating orreputation score for compliance and such data may be communicated to theretailer at the client's choosing.

FIG. 6 is a diagram illustrating elements or components that may bepresent in a computer device or system configured to implement a method,process, function, or operation in accordance with an embodiment of thesubject matter disclosed herein. In accordance with one or moreembodiments, the system, apparatus, methods, processes, functions,and/or operations may be wholly or partially implemented in the form ofa set of instructions executed by one or more programmed computerprocessors such as a central processing unit (CPU) or microprocessor.Such processors may be incorporated in an apparatus, server, client orother computing or data processing device operated by, or incommunication with, other components of the system. As an example, FIG.6 is a diagram illustrating elements or components that may be presentin a computer device or system 600 configured to implement a method,process, function, or operation in accordance with an embodiment. Thesubsystems shown in FIG. 6 are interconnected via a system bus 602.Additional subsystems include a printer 604, a keyboard 606, a fixeddisk 608, and a monitor 610, which is coupled to a display adapter 612.Peripherals and input/output (I/O) devices, which couple to an I/Ocontroller 614, can be connected to the computer system by any number ofmeans known in the art, such as a serial port 616. For example, theserial port 616 or an external interface 618 can be utilized to connectthe computer device 600 to further devices and/or systems not shown inFIG. 6 including a wide area network such as the Internet, a mouse inputdevice, and/or a scanner. The interconnection via the system bus 602allows one or more processors 620 to communicate with each subsystem andto control the execution of instructions that may be stored in a systemmemory 622 and/or the fixed disk 608, as well as the exchange ofinformation between subsystems. The system memory 622 and/or the fixeddisk 608 may embody a tangible computer-readable medium.

It should be understood that the present disclosure as described abovecan be implemented in the form of control logic using computer softwarein a modular or integrated manner. Based on the disclosure and teachingsprovided herein, a person of ordinary skill in the art will know andappreciate other ways and/or methods to implement the present disclosureusing hardware and a combination of hardware and software.

Any of the software components, processes or functions described in thisapplication may be implemented as software code to be executed by aprocessor using any suitable computer language such as, for example,Java, JavaScript, C++ or Perl using, for example, conventional orobject-oriented techniques. The software code may be stored as a seriesof instructions, or commands on a computer readable medium, such as arandom access memory (RAM), a read only memory (ROM), a magnetic mediumsuch as a hard-drive or a floppy disk, or an optical medium such as aCD-ROM. Any such computer readable medium may reside on or within asingle computational apparatus, and may be present on or withindifferent computational apparatuses within a system or network.

All references, including publications, patent applications, andpatents, cited herein are hereby incorporated by reference to the sameextent as if each reference were individually and specifically indicatedto be incorporated by reference and/or were set forth in its entiretyherein.

The use of the terms “a” and “an” and “the” and similar referents in thespecification and in the following claims are to be construed to coverboth the singular and the plural, unless otherwise indicated herein orclearly contradicted by context. The terms “having,” “including,”“containing” and similar referents in the specification and in thefollowing claims are to be construed as open-ended terms (e.g., meaning“including, but not limited to,”) unless otherwise noted. Recitation ofranges of values herein are merely indented to serve as a shorthandmethod of referring individually to each separate value inclusivelyfalling within the range, unless otherwise indicated herein, and eachseparate value is incorporated into the specification as if it wereindividually recited herein. All methods described herein can beperformed in any suitable order unless otherwise indicated herein orclearly contradicted by context. The use of any and all examples, orexemplary language (e.g., “such as”) provided herein, is intended merelyto better illuminate embodiments and does not pose a limitation to thescope of the disclosure unless otherwise claimed. No language in thespecification should be construed as indicating any non-claimed elementas essential to each embodiment of the present disclosure.

Different arrangements of the components depicted in the drawings ordescribed above, as well as components and steps not shown or describedare possible. Similarly, some features and sub-combinations are usefuland may be employed without reference to other features andsub-combinations. Embodiments have been described for illustrative andnot restrictive purposes, and alternative embodiments will becomeapparent to readers of this patent. Accordingly, the present subjectmatter is not limited to the embodiments described above or depicted inthe drawings, and various embodiments and modifications can be madewithout departing from the scope of the claims below.

That which is claimed is:
 1. A computer-based method, comprising:establishing an observation campaign at a data observation computingplatform executing a real-world observance opportunity application forthe collection of real-world data regarding a product or service;identifying one or more users for participation in the observationcampaign, each user having a mobile computing device executing areal-world data observation application thereon that is communicativelycoupled to the data observation computing platform executing thereal-world observance opportunity application; sending at least onecommunication as a first push notification to at least one computingdevice of at least one user; the communication including a level ofcompensation offered to a user for fulfillment of a real-worldobservance opportunity, wherein sending the electronic communicationcorresponding to the opportunity is based upon a detected proximity ofthe mobile computing device to a location associated with theopportunity; in response to the at least one communication of thereal-world observance opportunity, obtaining real-world data related tothe product or service from the one or more users through activitydirected by the user of a mobile computing device, the obtainedreal-world data comprising audio visual data about the product and metadata about the location of the product collected using the mobilecomputing device, the real-world data received through an electroniccommunication from at least one of the mobile computing devices;obtaining sales data related to the product or service from a merchantcomputer that is associated with a coordinator of the observationcampaign, the sales data received through an electronic communicationfrom the merchant computer; in response to obtaining the sales data andin response to obtaining the real-world data, correlating the sales datawith collected real-world data to identify a correlation between thereal-world data received and the sales data in response to receiving thereal-word data and sales data; modifying the level of compensationoffered for fulfillment of the real-world observance opportunity inresponse to the correlation and communicating the modification to themerchant computer contemporaneously with the correlation; updating theone or more mobile computing devices using a second push notificationwith the modified level of compensation.
 2. The method of claim 1,wherein establishing the observation campaign includes specifying one ormore parameters of the campaign.
 3. The method of claim 2, wherein themodifying further includes modifying one or more parameters of thecampaign.
 4. The method of claim 1, wherein identifying one or moreusers for participation in the observation campaign further comprises:sending an electronic communication corresponding to an opportunity toobserve real-world data to a set of users, the electronic communicationsent to one or more remote computing devices associated with each userand sent from a data observation computing platform; and receiving aresponse to the electronic communication from one or more of the usersto which the communication was sent.
 5. The method of claim 1, whereinthe collected real-world data includes one or more of an image of aproduct being displayed at a specific location; an image of an areaaround a product being displayed at a specific location; or a user'scomments or observations regarding a product being displayed at aspecific location.
 6. The method of claim 1, wherein correlating thesales data with collected real-world data further comprises determininga relationship between sales of the product or service and one or moreof the time period before, during, or after the collection of thereal-world data.
 7. The method of claim 1, wherein the modifying furthercomprises one or more of: a total number of observations desired; a typeof data being collected; a placement of the product; a placement ofcollateral information regarding the product or service; and a price ofthe product or service.
 8. The method of claim 1, further comprisingmaking a list of available observation campaign opportunities availableto one or more prospective users or observers on a webpage.
 9. Acomputing system, comprising: a user-based mobile computing deviceconfigured to execute an observation application to coordinate observingand collecting of real-world data, the observing including collection ofaudio visual data about a product and meta data about a location of theproduct; an observation server computer configured to push a firstnotification that includes one or more opportunities for observations ofreal-world data to the user-based mobile computing device and configuredto receive real-world data observed and collected by the user-basedmobile computing device in response to the push notification, whereinthe observation server computer is further configured to send the firstpush notification corresponding to the opportunity based upon a detectedproximity of the user-based mobile computing device to a locationassociated with the opportunity; a sales correlation engine having acomputer-based method executing on the observation server computer toprocess the real-world data observed and collected by the user-basedmobile computing device in response to receiving the real-world data,the processing of the real-world data including accessing sales relateddata for a product or service for which the real-world data is beingcollected; correlating the accessed sales related data with the receivedreal-world data; and in response to the correlation, modifying a levelof compensation offered for fulfillment of the one or more opportunitiesfor observations of real-world data; and a merchant-computing devicecommunicatively coupled to the observation server computer andconfigured to generate and control a campaign of opportunities pushed bythe observation server computer, wherein the observation server computeris configured to push information about the modification to themerchant-computing device in response to the modification, wherein themerchant-computing device updates the one or more mobile computingdevices using a second push notification with the modified level ofcompensation.
 10. The computing system of claim 9, wherein thereal-world data includes one or more of: an image of a product beingdisplayed at a specific location; an image of an area around a productbeing displayed at a specific location; or a user's comments orobservations regarding a product being displayed at a specific location.11. The computing system of claim 9, wherein the observation servercomputer further comprises a module configured to determine if thereceived data from the user-based mobile computing device fulfills oneor more criteria for data collection.
 12. The computing system of claim9, wherein the user-based mobile computing device comprises one of thegroup comprised of: a mobile phone, a smart phone, a laptop computer,and handheld computer, a wearable computing device, and an augmentedreality device.
 13. The computing system of claim 9, wherein themodifying further includes modifying one or more parameters of thecampaign.
 14. The computing system of claim 9, wherein correlating thesales data with collected real-world data further comprises determininga relationship between sales of the product or service and one or moreof the time period before, during, or after the collection of thereal-world data.
 15. The computing system of claim 9, wherein themodifying further comprises one or more of: a total number ofobservations desired; a type of data being collected; a placement of theproduct; a placement of collateral information regarding the product orservice; and a price of the product or service.